Modelling maternal health data in the Philippines using machine learning

Date of Publication


Document Type

Master's Thesis

Degree Name

Master of Science in Information Technology


College of Computer Studies


Information Technology

Thesis Adviser

Jordan Aiko P. Deja


In this research, the author aims to provide an overview of the current Open Data (OD) initiative in the Philippines specific to Maternal Health Care (MHC). This research aims to show literary work and reviews on the use of Machine Learning techniques towards building a stronger Open Data framework. Open data availability accelerates rapidly in several governments globally. With its multiple proven beneficial advantages to a nation such as increased transparency and removal of silos it is without a doubt the direction to which developing countries will be going to achieve should development is required. This study summarizes the existing OD initiatives in the Philippines and their implementations and future direction. Once made publicly available on a country, OD does not solely function to be viewed, instead it requires even further analysis and richer visualization that should ideally result to recommendations of policy that aims to strengthen a nations commitment to its people and international neighbors. Alongside with continuous advent of OD, simultaneously Information and Communications Technology (ICT) rapidly influences the way every human performs his function. The conjunction of OD and ICT mainly relies on the latters ability to transform the vastly offered data of OD into useful information and advanced knowledge's. With many candidate ICT tools to choose from in applying to OD, this study explores the role of Machine Learning specifically its inception phase clustering in standing as a data to information transformation translator. Machine Learning requires vastly huge data entries to further teach a machine intelligently on how to process an input based from an acquired knowledge pattern. OD just gives that candidate, with its characteristic such as excessive and continuous, This study examines the union of ML and OD result in an improved outcome in achieving the Sustainable Development Goals by the United Nations. In an attempt to seek the implications of this, the author employ Machine Learning in form of clustering to create a Data Model based from a strong OD based dataset, which can provide analysis and visualization tool for these open datasets.

Abstract Format






Accession Number


Shelf Location

Archives, The Learning Commons, 12F Henry Sy Sr. Hall

Physical Description

1 computer discs ; 4 3/4 inches


Maternal health services--Philippines; Machine learning

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